Wind Distribution

Statistical wind speed distribution are used to compute energy output from a wind turbine.

The well known weibull distribution is mostly used to fit data.

Use your own data

Anemometer data to create a wind distribution that will fit more precisely your own data.

Note

Look at WMO recommendation on how to measure winds. WMO recommends 10 min averages data points.

Averaging periods shorter than a few minutes do not sufficiently smooth the usually occurring natural turbulent fluctuations of wind

Wind stats can generate a wind distribution from your data using a Kernel Density Estimator (KDE). Wind speed distribution is scaled with vertical wind log profile if anemometer height & wind turbine height are different.

from wind_stats import WindDistribution

WindDistribution.from_data(data, roughness_length, measurement_height, height)

Other Statistical distribution

Wind stats uses scipy under the hood, so if another statistical distribution fits your need you can create it. Just create a WindDistribution with any continuous distribution in scipy.

https://docs.scipy.org/doc/scipy/reference/stats.html#continuous-distributions

In [1]: from scipy.stats import rayleigh

In [2]: from wind_stats import WindDistribution

In [3]: wind_distribution = WindDistribution(rayleigh(1, 5))

In [4]: wind_distribution
Out[4]: <WindDistribution>(type: rayleigh, mean: 7.2666 m/s)

In [5]: from matplotlib import pyplot as plt

In [6]: import numpy as np

In [7]: x = np.linspace(1, 25)

In [8]: y = wind_distribution.pdf(x)

In [9]: plt.plot(x, y)
Out[9]: [<matplotlib.lines.Line2D at 0x7fd8ff43bc70>]

Todo

Wind Distribution user guide under construction.

Roughness length API Reference